Author:

Keywords:

SISTA, Science & Technology, Technology, Engineering, Multidisciplinary, Instruments & Instrumentation, Engineering, magnetic resonance spectroscopy, metabolites, continuous wavelet transform, metabolite-based autocorrelation wavelets, FREQUENCY-DOMAIN METHODS, DESIGNING WAVELETS, NMR-SPECTROSCOPY, SPECIFIED SIGNAL, QUANTITATION, ALGORITHMS, TIME, MATCH, 0203 Classical Physics, 0906 Electrical and Electronic Engineering, Applied Physics, 4008 Electrical engineering, 4009 Electronics, sensors and digital hardware

Abstract:

We introduce a new class of wavelets, called metabolite-based autocorrelation wavelets, for the analysis of magnetic resonance spectroscopic (MRS) signals by means of the continuous wavelet transform (CWT). Each MRS signal consists of a number of frequency components typical for the active nuclei and the chemical environment around them in a particular voxel. Identifying individual metabolite components is crucial for the evolving field of MRS for clinical applications. In a first step, we develop the theoretical analysis, considering continuous wavelets derived from (Lorentzian lineshape) signal models. With this analytical approach, we can not only tailor individual wavelets but also determine signal parameters such as the damping factor of the Lorentzian lineshape. Then, we design more complex wavelets numerically from discrete metabolite profiles. As the resulting wavelets are discrete, too, they require an extra step of up- and downsampling in order to perform a proper CWT. The outcome is that the present analysis indicates without ambiguity the presence of a given metabolite in a MRS signal. © 2011 IOP Publishing Ltd.